The present invention pertains in general to modeling techniques and, more particularly, to combining steady-state and dynamic models for the purpose of prediction, control and optimization.
Process models that are utilized for prediction, control and optimization can be divided into two general categories, steady-state models and dynamic models. In each case the model is a mathematical construct that characterizes the process, and process measurements are utilized to parameterize or fit the model so that it replicates the behavior of the process. The mathematical model can then be implemented in a simulator for prediction or inverted by an optimization algorithm for control or optimization.
Steady-state or static models are utilized in modem process control systems that usually store a great deal of data, this data typically containing steady-state information at many different operating conditions. The steady-state information is utilized to train a non-linear model wherein the process input variables are represented by the vector U that is processed through the model to output the dependent variable Y. The non-linear model is a steady-state phenomenological or empirical model developed utilizing several ordered pairs (Ui, Yi) of data from different measured steady states. If a model is represented as:
Y=P(U, Y)xe2x80x83xe2x80x83(001)
where P is some parameterization, then the steady-state modeling procedure can be presented as:
({right arrow over (U)}, {right arrow over (Y)})xe2x86x92Pxe2x80x83xe2x80x83(002)
where U and Y are vectors containing the Ui, Yi ordered pair elements. Given the model P, then the steady-state process gain can be calculated as:                     K        =                              Δ            ⁢                          xe2x80x83                        ⁢                          P              ⁡                              (                                  U                  ,                  Y                                )                                                          Δ            ⁢                          xe2x80x83                        ⁢            U                                              (        003        )            
The steady-state model therefore represents the process measurements that are taken when the system is in a xe2x80x9cstaticxe2x80x9d mode. These measurements do not account for the perturbations that exist when changing from one steady-state condition to another steady-state condition. This is referred to as the dynamic part of a model.
A dynamic model is typically a linear model and is obtained from process measurements which are not steady-state measurements; rather, these are the data obtained when the process is moved from one steady-state condition to another steady-state condition. This procedure is where a process input or manipulated variable u(t) is input to a process with a process output or controlled variable y(t) being output and measured. Again, ordered pairs of measured data (u(I), y(I)) can be utilized to parameterize a phenomenological or empirical model, this time the data coming from non-steady-state operation. The dynamic model is represented as:
y(t)=p(u(t), y(t))xe2x80x83xe2x80x83(004)
where p is some parameterization. Then the dynamic modeling procedure can be represented as:
({right arrow over (u)}, {right arrow over (y)})xe2x86x92pxe2x80x83xe2x80x83(005)
Where u and y are vectors containing the (u(I), y(I)) ordered pair elements. Given the model p, then the steady-state gain of a dynamic model can be calculated as:                     k        =                              Δ            ⁢                          xe2x80x83                        ⁢                          p              ⁡                              (                                  u                  ,                  y                                )                                                          Δ            ⁢                          xe2x80x83                        ⁢            u                                              (        006        )            
Unfortunately, almost always the dynamic gain k does not equal the steady-state gain K, since the steady-state gain is modeled on a much larger set of data, whereas the dynamic gain is defined around a set of operating conditions wherein an existing set of operating conditions are mildly perturbed. This results in a shortage of sufficient non-linear information in the dynamic data set in which non-linear information is contained within the static model. Therefore, the gain of the system may not be adequately modeled for an existing set of steady-state operating conditions. Thus, when considering two independent models, one for the steady-state model and one for the dynamic model, there is a mis-match between the gains of the two models when used for prediction, control and optimization. The reason for this mis-match are that the steady-state model is non-linear and the dynamic model is linear, such that the gain of the steady-state model changes depending on the process operating point, with the gain of the linear model being fixed. Also, the data utilized to parameterize the dynamic model do not represent the complete operating range of the process, i.e., the dynamic data is only valid in a narrow region. Further, the dynamic model represents the acceleration properties of the process (like inertia) whereas the steady-state model represents the tradeoffs that determine the process final resting value (similar to the tradeoff between gravity and drag that determines terminal velocity in free fall).
One technique for combining non-linear static models and linear dynamic models is referred to as the Hammerstein model. The Hammerstein model is basically an input-output representation that is decomposed into two coupled parts. This utilizes a set of intermediate variables that are determined by the static models which are then utilized to construct the dynamic model. These two models are not independent and are relatively complex to create.
The present invention disclosed and claimed herein comprises a method and apparatus for controlling the operation of a plant by predicting a change in the dynamic input values to the plant to effect a change in the output from a current output value at a first time to a desired output value at a second time. The controller includes a dynamic predictive model fore receiving the current input value and the desired output value and predicting a plurality of input values at different time positions between the first time and the second time to define a dynamic operation path of the plant between the current output value and the desired output value at the second time. An optimizer then optimizes the operation of the dynamic controller at each of the different time positions from the first time to the second time in accordance with a predetermined optimization method that optimizes the objectives of the dynamic controller to achieve a desired path. This allows the objectives of the dynamic predictive model to vary as a function of time.
In another aspect of the present invention, the dynamic model includes a dynamic forward model operable to receive input values at each of the time positions and map the received input values through a stored representation of the plant to provide a predicted dynamic output value. An error generator then compares the predicted dynamic output value to the desired output value and generates a primary error value as a difference therebetween for each of the time positions. An error minimization device then determines a change in the input value to minimize the primary error value output by the error generator. A summation device sums the determined input change value with the original input value for each time position to provide a future input value, with a controller controlling the operation of the error minimization device and the optimizer. This minimizes the primary error value in accordance with the predetermined optimization method.
In a yet another aspect of the present invention, the controller is operable to control the summation device to iteratively minimize the primary error value by storing the summed output value from the summation device in a first pass through the error minimization device and then input the latch contents to the dynamic forward model in subsequent passes and for a plurality of subsequent passes. The output of the error minimization device is then summed with the previous contents of the latch, the latch containing the current value of the input on the first pass through the dynamic forward model and the error minimization device. The controller outputs the contents of the latch as the input to the plant after the primary error value has been determined to meet the objectives in accordance with the predetermined optimization method.
In a further aspect of the present invention, a gain adjustment device is provided to adjust the gain of the linear model for substantially all of the time positions. This gain adjustment device includes a non-linear model for receiving an input value and mapping the received input value through a stored representation of the plant to provide on the output thereof a predicted output value, and having a non-linear gain associated therewith. The linear model has parameters associated therewith that define the dynamic gain thereof with a parameter adjustment device then adjusting the parameters of the linear model as a function of the gain of the non-linear model for at least one of the time positions.
In yet a further aspect of the present invention, the gain adjustment device further allows for approximation of the dynamic gain for a plurality of the time positions between the value of the dynamic gain at the first time and the determined dynamic gain at one of the time positions having the dynamic gain thereof determined by the parameter adjustment device. This one time position is the maximum of the time positions at the second time.
In yet another aspect of the present invention, the error minimization device includes a primary error modification device for modifying the primary error to provide a modified error value. The error minimization device optimizes the operation of the dynamic controller to minimize the modified error value in accordance with the predetermined optimization method. The primary error is weighted as a function of time from the first time to the second time, with the weighting function decreasing as a function of time such that the primary error value is attenuated at a relatively high value proximate to the first time and attenuated at a relatively low level proximate to the second time.
In yet a further aspect of the present invention, a predictive system is provided for predicting the operation of a plant with the predictive system having an input for receiving input value and an output for providing a predicted output value. The system includes a non-linear model having an input for receiving the input value and mapping it across a stored learned representation of the plant to provide a predicted output. The non-linear model has an integrity associated therewith that is a function of a training operation that varies across the mapped space. A first principles model is also provided for providing a calculator representation of the plant. A domain analyzer determines when the input value falls within a region of the mapped space having an integrity associated therewith that is less than a predetermined integrity threshold. A domain switching device is operable to switch operation between the non-linear model and the first principles model as a function of the determined integrity level comparison with the predetermined threshold. If it is above the integrity threshold, the non-linear model is utilized and, if it is below the integrity threshold, the first principles model is utilized.